PDF(Pubmed)
Abstract：
This study investigates the clustering patterns of human β-secretase 1 (BACE-1) inhibitors using complex network methodologies based on various distance functions, including Euclidean, Tanimoto, Hamming, and Levenshtein distances. Molecular descriptor vectors such as molecular mass, Merck Molecular Force Field (MMFF) energy, Crippen partition coefficient (ClogP), Crippen molar refractivity (MR), eccentricity, Kappa indices, Synthetic Accessibility Score, Topological Polar Surface Area (TPSA), and 2D/3D autocorrelation entropies are employed to capture the diverse properties of these inhibitors. The Euclidean distance network demonstrates the most reliable clustering results, with strong agreement metrics and minimal information loss, indicating its robustness in capturing essential structural and physicochemical properties. Tanimoto and Hamming distance networks yield valuable clustering outcomes, albeit with moderate performance, while the Levenshtein distance network shows significant discrepancies. The analysis of eigenvector centrality across different networks identifies key inhibitors acting as hubs, which are likely critical in biochemical pathways. Community detection results highlight distinct clustering patterns, with well-defined communities providing insights into the functional and structural groupings of BACE-1 inhibitors. The study also conducts non-parametric tests, revealing significant differences in molecular descriptors, validating the clustering methodology. Despite its limitations, including reliance on specific descriptors and computational complexity, this study offers a comprehensive framework for understanding molecular interactions and guiding therapeutic interventions. Future research could integrate additional descriptors, advanced machine learning techniques, and dynamic network analysis to enhance clustering accuracy and applicability.
摘要：
这项研究使用基于各种距离函数的复杂网络方法研究了人β-分泌酶1（BACE-1）抑制剂的聚类模式，包括欧几里得,Tanimoto,Hamming,和Levenshtein距离。分子描述符载体，如分子质量，默克分子力场(MMFF)能量,克里彭分配系数(ClogP)，Crippen磨牙屈光度（MR），偏心率,Kappa指数,综合可达性评分,拓扑极表面积(TPSA),和2D/3D自相关熵用于捕获这些抑制剂的不同性质。欧氏距离网络展示了最可靠的聚类结果，具有强大的协议度量和最小的信息损失，表明其在捕获基本结构和物理化学性质方面的稳健性。Tanimoto和Hamming距离网络产生有价值的聚类结果，尽管表现适中，而Levenshtein距离网络显示出明显的差异。对不同网络的特征向量中心性的分析确定了充当枢纽的关键抑制因素，这可能是生化途径的关键。社区检测结果突出了不同的聚类模式，明确定义的社区提供对BACE-1抑制剂的功能和结构分组的见解。该研究还进行了非参数检验，揭示了分子描述符的显著差异，验证聚类方法。尽管有其局限性,包括对特定描述符和计算复杂性的依赖，本研究为理解分子间相互作用和指导治疗干预提供了一个全面的框架.未来的研究可以整合额外的描述符，先进的机器学习技术，和动态网络分析，以提高聚类的准确性和适用性。
